The following relates generally to medical imaging. It finds particular application in conjunction with magnetic resonance imaging, image reconstruction, and non-rigid motion artifact reduction, and will be described with particular reference thereto. However, it will be understood that it also finds application in other usage scenarios and is not necessarily limited to the aforementioned application.
Magnetic resonance (MR) imaging provides detailed anatomical and metabolic information of a subject. MR imaging involves no ionizing radiation and works by exciting magnetic resonance in tissue of the subject. Magnetic resonance occurs within a static main field B0 which is typically oriented horizontally or vertically. Radio frequency (RF) pulses are applied to excite resonance. Gradient fields are applied across the static field to focus and manipulate resonance in the subject. The local coils receive the weak magnetic resonance decay RF signals close to the body and retransmit the received signals to a receiver. The magnetic field direction of the received RF field is orthogonal to the magnetic field direction of the main field (B0). The received RF or magnetic resonance (MR) data is received into k-space or a memory of the spatial frequencies. The MR data in k-space is reconstructed into one or more images.
During the imaging process, the received MR data is susceptible to motion artifacts. Motion is classified as rigid motion such as nodding the head and non-rigid motion such as eye movement. Rigid motion can be compensated for by techniques which use the rigid parts of the body such as bone to properly reorient the MR data. For example, rotation angles and translation distances can be used to compensate for nodding. However, non-rigid motion remains such as eye movement which includes eye ball rolling, skin movement which includes frowning, jaw movement which includes swallowing and/or yawning, and the like. Non-rigid motion can cause spatially localized artifacts. With non-rigid motion, most of the image has good image quality with a high signal to noise ratio (SNR), but some portions of the image include motion artifacts.
One approach is to simply re-run the imaging sequence, which uses valuable clinical time. Another approach is to reject the portions of k-space data which include motion defects, and then reconstruct an image using motion free k-space. Algorithms such as data convolution and combination operation (COCOA) are used to detect motion in k-space and reject the portions of k-space which include motion. Algorithms such as SENSE can be used to perform a partial k-space reconstruction into an image. However, reconstructions of partial k-space typically yield low image quality due to a high reduction factor and data missing in the center of k-space. The result includes a low SNR, but a motion free image.
One consequence of partial k-space reconstructions is image aliasing. Image aliasing occurs as a result of removing the portions of k-space which analogously result in a negative of the motion artifact due to the loss of the portions of k-space and the reduced SNR.